US9427201B2 - Non-invasive method for using 2D angiographic images for radiosurgical target definition - Google Patents
Non-invasive method for using 2D angiographic images for radiosurgical target definition Download PDFInfo
- Publication number
- US9427201B2 US9427201B2 US11/823,932 US82393207A US9427201B2 US 9427201 B2 US9427201 B2 US 9427201B2 US 82393207 A US82393207 A US 82393207A US 9427201 B2 US9427201 B2 US 9427201B2
- Authority
- US
- United States
- Prior art keywords
- angiographic
- images
- angiographic images
- object space
- imaging system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active, expires
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000003384 imaging method Methods 0.000 claims abstract description 126
- 230000005855 radiation Effects 0.000 claims abstract description 33
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims description 39
- 230000009466 transformation Effects 0.000 claims description 36
- 210000005166 vasculature Anatomy 0.000 claims description 34
- 239000002872 contrast media Substances 0.000 claims description 30
- 238000011524 similarity measure Methods 0.000 claims description 19
- 208000022211 Arteriovenous Malformations Diseases 0.000 claims description 14
- 230000005744 arteriovenous malformation Effects 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 14
- 210000003484 anatomy Anatomy 0.000 claims description 12
- 238000002347 injection Methods 0.000 claims description 6
- 239000007924 injection Substances 0.000 claims description 6
- 238000009877 rendering Methods 0.000 claims description 5
- 238000001802 infusion Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims 1
- 238000002591 computed tomography Methods 0.000 description 20
- 238000002059 diagnostic imaging Methods 0.000 description 12
- 230000015654 memory Effects 0.000 description 12
- 230000001575 pathological effect Effects 0.000 description 10
- 210000001367 artery Anatomy 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000000926 separation method Methods 0.000 description 7
- 210000001519 tissue Anatomy 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 5
- 238000013519 translation Methods 0.000 description 5
- 230000014616 translation Effects 0.000 description 5
- 210000003462 vein Anatomy 0.000 description 5
- 239000008280 blood Substances 0.000 description 4
- 210000004369 blood Anatomy 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 238000004171 remote diagnosis Methods 0.000 description 4
- 238000009826 distribution Methods 0.000 description 3
- 230000004927 fusion Effects 0.000 description 3
- 230000008676 import Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 238000000844 transformation Methods 0.000 description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 230000000740 bleeding effect Effects 0.000 description 2
- 210000004204 blood vessel Anatomy 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000013170 computed tomography imaging Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000002595 magnetic resonance imaging Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 238000002600 positron emission tomography Methods 0.000 description 2
- 210000003625 skull Anatomy 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000002792 vascular Effects 0.000 description 2
- 208000029767 Congenital, Hereditary, and Neonatal Diseases and Abnormalities Diseases 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 239000000853 adhesive Substances 0.000 description 1
- 230000001070 adhesive effect Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 238000002583 angiography Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 238000005266 casting Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- NJPPVKZQTLUDBO-UHFFFAOYSA-N novaluron Chemical compound C1=C(Cl)C(OC(F)(F)C(OC(F)(F)F)F)=CC=C1NC(=O)NC(=O)C1=C(F)C=CC=C1F NJPPVKZQTLUDBO-UHFFFAOYSA-N 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 208000024335 physical disease Diseases 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000000275 quality assurance Methods 0.000 description 1
- 238000002673 radiosurgery Methods 0.000 description 1
- 238000001959 radiotherapy Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/50—Clinical applications
- A61B6/504—Clinical applications involving diagnosis of blood vessels, e.g. by angiography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computerised tomographs
- A61B6/032—Transmission computed tomography [CT]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/46—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with special arrangements for interfacing with the operator or the patient
- A61B6/461—Displaying means of special interest
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/46—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with special arrangements for interfacing with the operator or the patient
- A61B6/461—Displaying means of special interest
- A61B6/466—Displaying means of special interest adapted to display 3D data
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/48—Diagnostic techniques
- A61B6/481—Diagnostic techniques involving the use of contrast agents
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/54—Control of apparatus or devices for radiation diagnosis
- A61B6/547—Control of apparatus or devices for radiation diagnosis involving tracking of position of the device or parts of the device
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/58—Testing, adjusting or calibrating apparatus or devices for radiation diagnosis
- A61B6/582—Calibration
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/58—Testing, adjusting or calibrating apparatus or devices for radiation diagnosis
- A61B6/587—Alignment of source unit to detector unit
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
- A61B90/39—Markers, e.g. radio-opaque or breast lesions markers
-
- G06T7/0028—
-
- G06T7/0038—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/38—Registration of image sequences
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
- A61B90/39—Markers, e.g. radio-opaque or breast lesions markers
- A61B2090/3966—Radiopaque markers visible in an X-ray image
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/022—Stereoscopic imaging
Definitions
- Embodiments of the present invention are related to the field of medical imaging and data fusion, in particular, to non-invasive methods and apparatus for combining 2D angiographic images with 3D scan data for radiosurgical target definition.
- External beam radiation treatment is a non-invasive treatment method for pathological anatomies such as benign or malignant tumors, lesions and arteriovenous malformations (AVMs), which use a precisely positioned radiation beam to necrotize pathological tissue.
- pathological anatomies such as benign or malignant tumors, lesions and arteriovenous malformations (AVMs)
- AFMs arteriovenous malformations
- an external radiation source is mounted in a gantry that is rotated around a center of treatment (isocenter) and directs a sequence of x-ray beams at a pathological anatomy from multiple angles, with the patient positioned so the pathological anatomy is at the isocenter.
- every beam passes through the pathological anatomy, but passes through a different area of healthy tissue on its way to the pathological anatomy.
- the radiation source includes a multi-leaf collimator (MLC) that may be used to shape the radiation beam.
- MLC multi-leaf collimator
- the radiation source is mounted on a robotic control arm with multiple degrees of freedom, allowing the treatment to be non-isocentric to achieve better dose conformality and homogeneity relative to isocentric systems.
- a medical physicist determines the appropriate radiation dose for the pathological anatomy and plans the sequence of radiation treatment beams (e.g., position, location, angle, duration and shape) to achieve the prescribed dose.
- the medical physicist determines parameters such as the trajectory and duration of the radiation beams to be applied to a pathological anatomy and then calculates how much radiation will be absorbed by pathological tissue, critical structures (i.e., vital organs) and other healthy tissue.
- the parameters describing the beams may then be successively updated by the physicist until the radiation dose distribution is deemed acceptable.
- inverse planning in contrast to forward planning, the medical physicist specifies the minimum dose to the tumor and the maximum dose to other healthy tissues independently, and the treatment planning software then selects the direction, distance, and total number and energy of the beams in order to achieve the specified dose conditions.
- CT computerized x-ray tomography
- An AVM is a congenital disorder of the connections between veins and arteries in the vascular system.
- the arteries in the vascular system carry oxygen-rich blood at a relatively high pressure.
- arteries divide and sub-divide repeatedly, eventually forming a sponge-like capillary bed. Blood moves through the capillaries, giving up oxygen and taking up waste products from the surrounding cells. Capillaries successively join together, one upon the other, to form the veins that carry blood away at a relatively low pressure.
- the arteries are connected directly to the veins in a tangled interconnection and the capillary bed is missing.
- the tangle of blood vessels forms a relatively direct connection between high pressure arteries and low pressure veins.
- This collection of blood vessels known as a nidus, can be extremely fragile and prone to bleeding.
- AVMs can occur in various parts of the body including the brain, where bleeding can cause severe and often fatal strokes. If detected before a stroke occurs, the AVM can be treated with external beam radiation. The radiation damages the walls of the veins and arteries of the nidus. In response, the walls thicken and grow in, eventually closing off the arteries feeding blood into the nidus.
- one of the goals of treatment planning is to identify the nidus of the AVM and to distinguish it from its feeding vessels.
- identifying the nidus and its feeder vessels in a CT scan is difficult because the target vasculature has very low contrast in the x-ray modality of CT scans.
- the patient can be injected with an x-ray contrast agent immediately prior to CT imaging.
- the 3D images generally show the AVM after the contrast agent has suffused the nidus. While it is sometimes possible to delineate the nidus from the 3D images, it may often be difficult to distinguish the feeding vessels from the nidus and to identify the boundary between the nidus and the feeding vessels.
- the patient may be imaged in a separate 2D angiographic imaging system, which may include a fixed x-ray source and detector or, alternatively, a source and detector that are movable around the patient to capture different views. Images can be acquired both before and after the injection of the contrast agent. The ‘before’ image can be subtracted from the ‘after’ image to produce a difference image known as a digital subtraction angiography (DSA) image.
- DSA digital subtraction angiography
- a rapid series of fixed, 2D x-ray projection images can be taken from the time the contrast agent is injected until it enters the nidus.
- the 2D images can then be examined after the fact to show the contrast agent advancing through the feeding vessels and entering the nidus.
- the image that best distinguishes the feeding vessels from the nidus can then be selected from the sequence.
- the 2D angiograms In order for the 2D angiograms to be useful for radiosurgical treatment planning, they need to be integrated with the 3D CT scan data.
- the imaging geometry of the angiographic imaging system e.g., imaging angles and source and detector separations
- the imaging geometry of the angiographic imaging system may be unknown with respect to the imaging geometry of the CT imaging system, so that the two sets of images cannot be directly integrated.
- the patient is fitted with an invasive frame that holds a configuration of fiducial markers.
- the attachment points of the frame are sharply pointed screws that pierce the skin and enter the skull of the patient.
- the fiducial markers then appear as landmarks in the angiographic images.
- the frame remains attached to the patient during a subsequent CT scan so that the landmarks appear in the CT images.
- Different slices of the CT image can then be iteratively compared with the angiographic images to find a matching orientation.
- the frame may also be required for patient alignment during treatment, requiring the patient to suffer the discomfort of the invasive frame continuously through the process of diagnostic imaging, treatment planning and treatment delivery.
- FIG. 1 illustrates an angiographic imaging system in one embodiment
- FIG. 2 illustrates an angiographic imaging system in another embodiment
- FIG. 3 illustrates a cranial arteriovenous malformation
- FIG. 4 illustrates the transformation parameters between an angiographic imaging system and a 3D imaging system in one embodiment
- FIG. 5A illustrates in-plane translation in 2D-2D registration in one embodiment
- FIG. 5B illustrates in-plane rotation in 2D-2D registration in one embodiment
- FIG. 5C illustrates a first out-of-plane rotation in 2D-2D registration in one embodiment
- FIG. 5D illustrates a second out-of-plane rotation in 2D-2D registration in one embodiment
- FIG. 6 is a flowchart illustrating six-parameter 2D to 3D registration in one embodiment
- FIG. 7 is a flowchart illustrating a method in one embodiment
- FIG. 8 is a flowchart illustrating a method in one embodiment
- FIG. 9 is a flowchart illustrating a method in one embodiment.
- FIG. 10 is a bock diagram illustrating a system in which embodiment of the invention may be implemented.
- image may mean a visible image (e.g., displayed on a video screen) or a digital representation of an image (e.g., a file corresponding to the pixel output of an image detector).
- terms such as “generating,” “registering,” “determining,” “aligning,” “positioning,” “processing,” “computing,” “selecting,” “estimating,” “comparing,” “tracking” or the like may refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
- Embodiments of the methods described herein may be implemented using computer software. If written in a programming language conforming to a recognized standard, sequences of instructions designed to implement the methods can be compiled for execution on a variety of hardware platforms and for interface to a variety of operating systems. In addition, embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement embodiments of the present invention.
- FIG. 1 illustrates an angiographic imaging system 100 in one embodiment.
- angiographic imaging system 100 includes an x-ray source 103 and an x-ray detector 104 that can be positioned in two (or more) different orientations, characterized by an angular separation, source to detector separation, intersection of the focal axis with the detector and detector pixel size, some or all of which may not be known a priori.
- a patient 108 is positioned on a patient couch 106 , with a fitted headrest (not shown) designed to keep the patient's head immobile.
- An array of non-invasive fiducial markers ( 109 ) is placed on the patient's head. The fiducial markers may be attached, for example, with adhesives.
- a plurality of 2D angiograms is acquired in two or more orientations of the angiographic imaging system, such that each of the plurality of 2D angiographic images includes a projection of the array of non-invasive fiducial markers.
- the patient may be transferred to a calibrated 3D imaging system (such as a CT system, for example), where a calibrated image of the patient, including the array of fiducial markers, can be acquired.
- the calibrated image may then be used to measure the 3D configuration of the array of fiducial markers.
- the imaging geometry of each of the orientations of the angiographic imaging system may be determined (i.e., the system may be calibrated) using algorithms that are known in the art (see, e.g., Roger E. Tsai, “A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses,” IEEE Journal of Robotics and Automation, August 1987).
- the attached array of fiducial markers 109 may be replaced with a non-invasive calibration device 110 having an array of non-invasive fiducial markers in a known 3D configuration.
- the imaging geometry of the angiographic imaging system may be determined directly from the known 3D configuration of the fiducial markers and the positions of the fiducial markers in the plurality of 2D angiographic images using the calibration algorithm.
- angiographic imaging system 100 may also include tracking detectors 107 A and 107 B.
- Tracking detectors 107 A and 107 B may be, for example, optical or magnetic tracking detectors as are known in the art.
- the non-invasive fiducial markers 109 and/or the non-invasive fiducial markers on the calibration device 110 may be optical or magnetic devices that may be tracked by tracking detectors 107 A and 107 B to determine the 3D configuration of the fiducial markers.
- the imaging geometry of the angiographic imaging system may be determined directly from the known (i.e. tracked) 3D configuration of the fiducial markers and the positions of the fiducial markers in the plurality of 2D angiographic images using the calibration algorithm.
- FIG. 3 is a schematic representation of an exemplary 2D angiogram in each of two orientations (views 301 and 302 , respectively) of angiographic imaging system 100 , illustrating a nidus 304 and feeder vessels 303 .
- the exemplary angiograms may be selected, for example, from one or more time-series of angiograms recording the progress of a contrast agent from its injection into the patient through its infusion of the nidus.
- the selected angiograms may be selected at a point in time where the contrast agent has just reached the nidus and defines the boundary points of the nidus in each of the 2D projections of the angiographic images.
- the boundary points can be connected to define a boundary contour in each projection.
- the contours of the nidus can be back-projected through the imaging geometry of each of the two (or more) orientations of the angiographic imaging system to render a bounding volume of the nidus in the 3D object space of the angiographic imaging system.
- the plurality of 2D angiographic images may be imported into a treatment planning system, registered with 3D scan data of the patient as described below and combined (fused) with the 3D scan data.
- Registration is the determination of a one-to-one mapping or transformation between the coordinates in one space and those in another space, such that points in the two spaces that correspond to the same anatomical point are mapped to each other.
- the transformation or mapping that the registration produces must be applied in a clinically meaningful way.
- fusion of one image with another image to which it has been registered and reformatted may be accomplished, for example, by simply summing intensity values in the two images voxel by voxel (a “voxel,” as known in the art, is a 3D volume element), by superimposing outlines (e.g., contours) from one image on the other image, by encoding one image in hue and the other in brightness in a color image, or by providing a pair of movable cursors on two views linked via the registering transformation so that the cursors are displayed at corresponding points.
- outlines e.g., contours
- the registration is the mapping that aligns the 3D coordinate system of the CT scan volume) with the 3D object space of the angiographic imaging system in which the 2D images were produced.
- the registration may be accomplished by comparing the 2D projection images from the angiographic imaging system with virtual 2D images synthesized from the 3D scan data, known as digitally reconstructed radiographs (DRRs).
- DRRs digitally reconstructed radiographs
- a DRR is a synthetic x-ray image generated by casting (mathematically projecting) rays through the 3D scan data, simulating the geometry of the angiographic imaging system.
- the resulting DRR then has the same scale and point of view as the angiographic imaging system, and can be compared with the 2D angiographic images to determine the position and orientation of the patient within the angiographic imaging system.
- Different patient poses in the angiographic imaging system are simulated by performing 3D transformations (rotations and translations) on the 3D imaging data before each DRR is generated.
- Each comparison of a 2D angiographic image with a DRR produces a similarity measure or equivalently, a difference measure, which can be used to search for a 3D transformation that produces a DRR with a higher similarity measure to the angiographic image.
- the similarity measure is sufficiently maximized (or equivalently, a difference measure is minimized)
- the corresponding 3D transformation can be used to align the 3D object space of the angiographic imaging system with the 3D scan volume.
- the two data sets can then be fused to define the target anatomy (e.g., the nidus) for treatment planning.
- FIG. 4 illustrates 3D transformation parameters between the 3D object space [X P ,Y P ,Z P ] of angiographic imaging system 100 having two 2D projections and a 3D coordinate system [X R ,Y R ,Z R ] associated with 3D scan data (in FIG. 4 , the x-coordinates of both coordinate systems are normal to, and pointing into the plane of FIG. 4 ).
- Projections A and B in FIG. 4 are associated with the two positions of detector 104 in imaging system 100 where S A and S B represent the two positions of x-ray source 103 .
- O A and O B are the centers of the imaging planes of the x-ray detector in the two positions.
- the projections A and B are viewed from the directions O A S A and O B S B , respectively.
- the angular separation of the two source-detector positions is shown as 90 degrees for ease of illustration, and the following equations are derived for this configuration.
- Other imaging geometries are possible and the corresponding equations may be derived in a straightforward manner by one having ordinary skill in the art.
- a 3D transformation may be defined from coordinate system [X P ,Y P ,Z P ] (having coordinates x′,y′,z′) to coordinate system [X R ,Y R ,Z R ] (having coordinates x,y,z) in FIG. 4 in terms of six parameters: three translations ( ⁇ x, ⁇ y, ⁇ z) and three rotations ( ⁇ x , ⁇ y , ⁇ z ).
- the 3D rigid transformation may be decomposed into an in-plane transformation ( ⁇ x A , ⁇ y A , ⁇ A ) and two out-of-plane rotations ( ⁇ x A , ⁇ y′ ).
- the decomposition consists of the in-plane transformation ( ⁇ x B , ⁇ y B , ⁇ B ) and two out-of-plane rotations ( ⁇ x B , ⁇ z′ ).
- 5A through 5D illustrate the in-plane transformations and out-of-plane rotations described herein, where a 2D x-ray image is represented by plane 51 and the 2D DRR is represented by plane 52 .
- the 3D rigid transformation of equation (1) may be simplified by noting that the use of two projections over-constrains the solution to the six parameters of the 3D rigid transformation.
- the translation x A in projection A is the same parameter as x B in projection B
- the out-of-plane rotation ⁇ x A in projection A is the same as ⁇ x B in projection B.
- ⁇ A and ⁇ B are geometric amplification factors (e.g., scale factors related to source-to-patient and patient-to-detector distances) for projections A and B, respectively.
- ⁇ x′ ( ⁇ B ⁇ x B ⁇ A ⁇ x A )/2
- ⁇ y′ ⁇ A ⁇ y A
- ⁇ z′ ⁇ B ⁇ y B .
- the 2D in-plane transformation ( ⁇ x A , ⁇ y A , ⁇ A ) may be estimated by a 2D to 2D image comparison, and the two out-of-plane rotations ( ⁇ x A , ⁇ y′ ) may be calculated by matching the angiographic image to the set of DRR images as described below, using similarity measures.
- the same process may be used to solve the 2D in-plane transformation ( ⁇ x B , ⁇ y B , ⁇ B ) and the out-of-plane rotations ( ⁇ x B , ⁇ z′ ) for the projection B.
- the in-plane transformation and out-of-plane rotations may be obtained by registration between the angiographic image and a DRR, independently for both projection A and projection B.
- the in-plane transformation can be approximately described by ( ⁇ x A , ⁇ y A , ⁇ A ) when ⁇ y′ is small (e.g., less than 5°).
- These methods generally employ the calculation of a similarity measure, followed by the application of a gradient search algorithm to maximize the similarity between the in-treatment x-ray images and selected DRRs.
- similarity measures include (but are not limited to) normalized cross-section, entropy of the difference image, mutual information, gradient correlation, pattern intensity and gradient difference.
- a corresponding simplification may be made for projection B.
- the six-parameter, 3D transformation required to align the 3D coordinate system of the angiographic imaging system with the 3D coordinate system of a 3D scan volume may be completely defined by the two sets of four parameters ( ⁇ x A , ⁇ y A , ⁇ A , ⁇ x A ) and ( ⁇ x B , ⁇ y B , ⁇ B , ⁇ x B ).
- the registration process described above is illustrated in the flowchart of FIG. 6 .
- the process begins with the acquisition of the 2D angiographic projection images in two orientations (operation 601 ).
- operation 602 2D angiographic projection images are compared and registered, as described above, with DRR sets created from 3D scan data, based on the derived imaging geometry of the angiographic imaging system.
- the results of the registration are the 2 sets of 2D transformation parameters that are used in operation 603 to calculate the six parameter, 3D transformation required in operation 404 to align the 3D object space of the angiographic imaging system with the 3D coordinate system of the 3D scan volume.
- DRRs synthetic x-rays
- the angiographic images are also x-rays and will have very similar intensity patterns everywhere except where the contrast agent is present. If the field of view of the DRRs and the angiographic images are large compared with the size of the nidus and the feeder vessels, then pattern intensity matching can be performed using images where contrast agent is present. In some cases, however (e.g., when the field of view is small an/or the nidus and feeder vessels dominate image, the presence of contrast agent may interfere with registration.
- the images with contrast agent may be replaced with images from the same orientation, but without the presence of contrast agent (e.g., images in a time-series taken before the injection of the contrast agent). Then, after the registration is performed as described above, the images with contrast agent may be used to define contours of the target vasculature (nidus) as described below.
- the 2D x-ray images in each projection of the x-ray imaging system may be combined for direct 2D-3D registration with the pre-operative 3D scan data as described in copending U.S. patent application Ser. No. 11/281,106.
- the transformation between the 3D object space of the angiographic imaging system and the 3D space of the CT scan volume may be applied to the 3D object space to align the bounding volume of the nidus of the AVM with the CT scan volume.
- the bounding volume may be used to define contours of the targeted vasculature (nidus) in 2D slices of the 3D scan volume in, for example, axial, sagittal and coronal views.
- the contours may be interpolated between slices of the CT scan volume to define the target for treatment planning and treatment delivery.
- FIG. 7 is a flowchart illustrating a method 700 in one embodiment of the present invention.
- the method begins by acquiring a plurality of two-dimensional (2D) angiographic images with two or more orientations of an angiographic imaging system, where each orientation has an unknown imaging geometry, and where each of the plurality of 2D angiographic images includes a projection of a plurality of non-invasive fiducial markers having a known three-dimensional (3D) configuration (operation 701 ).
- the method continues by determining the imaging geometry of each of the two or more orientations of the angiographic imaging system from the projections of the plurality of non-invasive fiducial markers in the 2D angiographic images and the known 3D configuration of the plurality of non-invasive fiducial markers (operation 702 ).
- the method continues by identifying contours of a target vasculature in one or more of the plurality of 2D angiographic images (operation 703 ), back-projecting the contours of the target vasculature, through the imaging geometry of the two or more orientations, to a 3D object space (operation 704 ) and rendering a volume of the target vasculature in the 3D object space (operation 705 ).
- the method concludes by registering selected 2D angiographic images to a 3D scan volume (operation 706 ).
- FIG. 8 is a flowchart illustrating a method 800 in another embodiment of the present invention.
- Method 800 begins by acquiring a plurality of two-dimensional (2D) angiographic images, with two or more orientations of an angiographic imaging system, each orientation having a known imaging geometry (operation 801 ).
- the method continues by identifying contours of a target vasculature in one or more of the plurality of 2D angiographic images (operation 802 ), back-projecting the contours of the target vasculature, through the imaging geometry of the two or more orientations of the angiographic imaging system, to a 3D object space (operation 803 ) and rendering a volume of the target vasculature in the 3D object space (operation 804 ).
- the method concludes by registering selected 2D angiographic images to a 3D scan volume with a six-parameter registration algorithm (operation 805 ).
- FIG. 9 is a flowchart illustrating a method 900 further to method 700 and/or method 800 in one embodiment.
- Method 900 begins at operation 901 , where the 3D object space of the angiographic imaging system is fused with the 3D scan volume.
- contours are generated in the 3D scan volume from the bounding volume of the target vasculature (nidus) in the 3D object space of the angiographic imaging system.
- the contours are used to develop the radiation treatment plan as described above.
- a reverse procedure may be used by a medical physicist that uses the 2D angiographic images as a quality assurance tool.
- the medical physicist may choose to identify contours of a target vasculature in the 3D scan volume.
- the contours of the target vasculature may then be projected through the imaging geometry of one or more orientations of the angiographic imaging system and displayed in the corresponding 2D angiographic image(s) to determine if the contours in the 3D scan volume conform with the target vasculature identified by contrast agent in the 2D angiographic images.
- FIG. 10 illustrates a system 950 in which embodiments of the present invention may be implemented.
- system 950 may include a diagnostic imaging system 1000 , a treatment planning system 2000 and a treatment delivery system 3000 .
- Diagnostic imaging system 1000 may be any system capable of producing medical diagnostic images of a patient that may be used for subsequent medical diagnosis, treatment planning and/or treatment delivery.
- diagnostic imaging system 1000 may be an angiographic imaging system (e.g., system 100 ), a computed tomography (CT) system, a magnetic resonance imaging (MRI) system, a positron emission tomography (PET) system, an ultrasound system or the like.
- CT computed tomography
- MRI magnetic resonance imaging
- PET positron emission tomography
- ultrasound system or the like.
- Diagnostic imaging system 1000 includes an imaging source 1010 to generate an imaging beam (e.g., x-rays) and an imaging detector 1020 to detect and receive the beam generated by imaging source 1010 .
- diagnostic imaging system 1000 may include two or more diagnostic X-ray sources and two or more corresponding imaging detectors.
- two x-ray sources may be disposed around a patient to be imaged, fixed at an angular separation from each other (e.g., 90 degrees, 45 degrees, etc.) and aimed through the patient toward (an) imaging detector(s) which may be diametrically opposed to the x-ray sources.
- a single large imaging detector, or multiple imaging detectors may also be used that would be illuminated by each x-ray imaging source.
- other numbers and configurations of imaging sources and imaging detectors may be used.
- the imaging source 1010 and the imaging detector 1020 may be coupled to a digital processing system 1030 to control the imaging operation and process image data.
- Diagnostic imaging system 1000 includes a bus or other means 1035 for transferring data and commands among digital processing system 1030 , imaging source 1010 and imaging detector 1020 .
- Digital processing system 1030 may include one or more general-purpose processors (e.g., a microprocessor), special purpose processor such as a digital signal processor (DSP) or other type of device such as a controller or field programmable gate array (FPGA).
- DSP digital signal processor
- FPGA field programmable gate array
- Digital processing system 1030 may also include other components (not shown) such as memory, storage devices, network adapters and the like.
- Digital processing system 1030 may be configured to generate digital diagnostic images in a standard format, such as the DICOM (Digital Imaging and Communications in Medicine) format, for example. In other embodiments, digital processing system 1030 may generate other standard or non-standard digital image formats. Digital processing system 1030 may transmit diagnostic image files (e.g., the aforementioned DICOM formatted files) to treatment planning system 2000 over a data link 1500 , which may be, for example, a direct link, a local area network (LAN) link or a wide area network (WAN) link such as the Internet. In addition, the information transferred between systems may either be pulled or pushed across the communication medium connecting the systems, such as in a remote diagnosis or treatment planning configuration. In remote diagnosis or treatment planning, a user may utilize embodiments of the present invention to diagnose or treatment plan despite the existence of a physical separation between the system user and the patient.
- DICOM Digital Imaging and Communications in Medicine
- Treatment planning system 2000 includes a processing device 2010 to receive and process image data, such as angiographic imaging data and 3D scan data as described above.
- Processing device 2010 may represent one or more general-purpose processors (e.g., a microprocessor), special purpose processor such as a digital signal processor (DSP) or other type of device such as a controller or field programmable gate array (FPGA).
- DSP digital signal processor
- FPGA field programmable gate array
- Processing device 2010 may be configured to execute instructions for performing treatment planning and/or image processing operations discussed herein, such as the spine segmentation tool described herein.
- Treatment planning system 2000 may also include system memory 2020 that may include a random access memory (RAM), or other dynamic storage devices, coupled to processing device 2010 by bus 2055 , for storing information and instructions to be executed by processing device 2010 .
- System memory 2020 also may be used for storing temporary variables or other intermediate information during execution of instructions by processing device 2010 .
- System memory 2020 may also include a read only memory (ROM) and/or other static storage device coupled to bus 2055 for storing static information and instructions for processing device 2010 .
- ROM read only memory
- Treatment planning system 2000 may also include storage device 2030 , representing one or more storage devices (e.g., a magnetic disk drive or optical disk drive) coupled to bus 2055 for storing information and instructions.
- Storage device 2030 may be used for storing instructions for performing the treatment planning steps discussed herein and/or for storing 3D imaging data and DRRs as discussed herein.
- Processing device 2010 may also be coupled to a display device 2040 , such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information (e.g., a 2D or 3D representation of the VOI) to the user.
- a display device 2040 such as a cathode ray tube (CRT) or liquid crystal display (LCD)
- An input device 2050 such as a keyboard, may be coupled to processing device 2010 for communicating information and/or command selections to processing device 2010 .
- One or more other user input devices e.g., a mouse, a trackball or cursor direction keys
- treatment planning system 2000 represents only one example of a treatment planning system, which may have many different configurations and architectures, which may include more components or fewer components than treatment planning system 2000 and which may be employed with the present invention. For example, some systems often have multiple buses, such as a peripheral bus, a dedicated cache bus, etc.
- the treatment planning system 2000 may also include MIRIT (Medical Image Review and Import Tool) to support DICOM import (so images can be fused and targets delineated on different systems and then imported into the treatment planning system for planning and dose calculations), expanded image fusion capabilities that allow the user to treatment plan and view dose distributions on any one of various imaging modalities (e.g., MRI, CT, PET, etc.).
- MIRIT Medical Image Review and Import Tool
- DICOM import so images can be fused and targets delineated on different systems and then imported into the treatment planning system for planning and dose calculations
- expanded image fusion capabilities that allow the user to treatment plan and view dose distributions on any one of various imaging modalities (e.g., MRI,
- Treatment planning system 2000 may share its database (e.g., data stored in storage device 2030 ) with a treatment delivery system, such as treatment delivery system 3000 , so that it may not be necessary to export from the treatment planning system prior to treatment delivery.
- Treatment planning system 2000 may be linked to treatment delivery system 3000 via a data link 2500 , which may be a direct link, a LAN link or a WAN link as discussed above with respect to data link 1500 .
- data links 1500 and 2500 are implemented as LAN or WAN connections, any of diagnostic imaging system 1000 , treatment planning system 2000 and/or treatment delivery system 3000 may be in decentralized locations such that the systems may be physically remote from each other.
- any of diagnostic imaging system 1000 , treatment planning system 2000 and/or treatment delivery system 3000 may be integrated with each other in one or more systems.
- Treatment delivery system 3000 includes a therapeutic and/or surgical radiation source 3010 to administer a prescribed radiation dose to a target volume in conformance with a treatment plan.
- Treatment delivery system 3000 may also include an imaging system 3020 to capture intra-treatment images of a patient volume (including the target volume) for registration or correlation with the diagnostic images described above in order to position the patient with respect to the radiation source.
- Imaging system 3020 may include any of the imaging systems described above.
- Treatment delivery system 3000 may also include a digital processing system 3030 to control radiation source 3010 , imaging system 3020 and a patient support device such as a treatment couch 3040 .
- Digital processing system 3030 may be configured to register 2D radiographic images from imaging system 3020 , from two or more stereoscopic projections, with digitally reconstructed radiographs (e.g., DRRs from segmented 3D imaging data) generated by digital processing system 1030 in diagnostic imaging system 1000 and/or DRRs generated by processing device 2010 in treatment planning system 2000 .
- Digital processing system 3030 may include one or more general-purpose processors (e.g., a microprocessor), special purpose processor such as a digital signal processor (DSP) or other type of device such as a controller or field programmable gate array (FPGA).
- Digital processing system 3030 may also include other components (not shown) such as memory, storage devices, network adapters and the like.
- Digital processing system 3030 may be coupled to radiation source 3010 , imaging system 3020 and treatment couch 3040 by a bus 3045 or other type of control and communication interface.
- Digital processing system 3030 may implement methods (e.g., such as method 1200 described above) to register images obtained from imaging system 3020 with pre-operative treatment planning images in order to align the patient on the treatment couch 3040 within the treatment delivery system 3000 , and to precisely position the radiation source with respect to the target volume.
- methods e.g., such as method 1200 described above
- the treatment couch 3040 may be coupled to another robotic arm (not illustrated) having multiple (e.g., 5 or more) degrees of freedom.
- the couch arm may have five rotational degrees of freedom and one substantially vertical, linear degree of freedom.
- the couch arm may have six rotational degrees of freedom and one substantially vertical, linear degree of freedom or at least four rotational degrees of freedom.
- the couch arm may be vertically mounted to a column or wall, or horizontally mounted to pedestal, floor, or ceiling.
- the treatment couch 3040 may be a component of another mechanical mechanism, such as the Axum® treatment couch developed by Accuray Incorporated of Delaware, or be another type of conventional treatment table known to those of ordinary skill in the art.
- treatment delivery system 3000 may be another type of treatment delivery system, for example, a gantry based (isocentric) intensity modulated radiotherapy (IMRT) system.
- a radiation source e.g., a LINAC
- LINAC a radiation source
- Radiation is then delivered from several positions on the circular plane of rotation.
- the shape of the radiation beam is defined by a multi-leaf collimator that allows portions of the beam to be blocked, so that the remaining beam incident on the patient has a pre-defined shape.
- the resulting system generates arbitrarily shaped radiation beams that intersect each other at the isocenter to deliver a dose distribution to the target region.
- the optimization algorithm selects subsets of the main beam and determines the amount of time that the patient should be exposed to each subset, so that the prescribed dose constraints are best met.
- the gantry based system may have a gimbaled radiation source head assembly.
- Embodiments of the present invention include various operations, which are described herein. These operations may be performed by hardware components, software, firmware or a combination thereof. Any of the signals provided over various buses described herein may be time multiplexed with other signals and provided over one or more common buses. Additionally, the interconnection between circuit components or blocks may be shown as buses or as single signal lines. Each of the buses may alternatively be one or more single signal lines and each of the single signal lines may alternatively be buses.
- Certain embodiments may be implemented as a computer program product that may include instructions stored on a machine-readable medium. These instructions may be used to program a general-purpose or special-purpose processor to perform the described operations.
- a machine-readable medium includes any mechanism for storing or transmitting information in a form (e.g., software, processing application) readable by a machine (e.g., a computer).
- the machine-readable medium may include, but is not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read-only memory (ROM); random-access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; electrical, optical, acoustical, or other form of propagated signal (e.g., carrier waves, infrared signals, digital signals, etc.); or another type of medium suitable for storing electronic instructions.
- magnetic storage medium e.g., floppy diskette
- optical storage medium e.g., CD-ROM
- magneto-optical storage medium e.g., magneto-optical storage medium
- ROM read-only memory
- RAM random-access memory
- EPROM and EEPROM erasable programmable memory
- flash memory electrical, optical, acoustical, or other form of propagated signal (e.g., carrier waves, in
- some embodiments may be practiced in distributed computing environments where the machine-readable medium is stored on and/or executed by more than one computer system.
- the information transferred between computer systems may either be pulled or pushed across the communication medium connecting the computer systems such as in a remote diagnosis or monitoring system.
- remote diagnosis or monitoring a user may diagnose or monitor a patient despite the existence of a physical separation between the user and the patient.
- the treatment delivery system may be remote from the treatment planning system.
Abstract
Description
x=x′,y=(y′−z′)/√{square root over (2)},z=(y′+z′)/√{square root over (2)},
θx=θx′,θy=(θy′−θz′)/√{square root over (2)},θz=(θy′+θz′)/√{square root over (2)}. (1)
Δx′=(αB Δx B−αA Δx A)/2,Δy′=α A Δy A ,Δz′=α B Δy B. (2)
Δθy′=ΔθB,Δθz′=ΔθA. (3)
Δx=(−αA Δx A+αB Δx B)/2,Δy=(αA αy A−αB Δy B)/√{square root over (2)},Δz=(αA Δy A+αB Δy B)/√{square root over (2)},Δθx=(Δθx
Claims (24)
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/823,932 US9427201B2 (en) | 2007-06-30 | 2007-06-30 | Non-invasive method for using 2D angiographic images for radiosurgical target definition |
US15/219,514 US11382588B2 (en) | 2007-06-30 | 2016-07-26 | Non-invasive method for using 2D angiographic images for radiosurgical target definition |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/823,932 US9427201B2 (en) | 2007-06-30 | 2007-06-30 | Non-invasive method for using 2D angiographic images for radiosurgical target definition |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/219,514 Continuation US11382588B2 (en) | 2007-06-30 | 2016-07-26 | Non-invasive method for using 2D angiographic images for radiosurgical target definition |
Publications (2)
Publication Number | Publication Date |
---|---|
US20090005668A1 US20090005668A1 (en) | 2009-01-01 |
US9427201B2 true US9427201B2 (en) | 2016-08-30 |
Family
ID=40161440
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/823,932 Active 2035-02-28 US9427201B2 (en) | 2007-06-30 | 2007-06-30 | Non-invasive method for using 2D angiographic images for radiosurgical target definition |
US15/219,514 Active 2031-02-12 US11382588B2 (en) | 2007-06-30 | 2016-07-26 | Non-invasive method for using 2D angiographic images for radiosurgical target definition |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/219,514 Active 2031-02-12 US11382588B2 (en) | 2007-06-30 | 2016-07-26 | Non-invasive method for using 2D angiographic images for radiosurgical target definition |
Country Status (1)
Country | Link |
---|---|
US (2) | US9427201B2 (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150063537A1 (en) * | 2013-08-29 | 2015-03-05 | Samsung Electronics Co., Ltd. | X-ray imaging apparatus and control method thereof |
US20150164457A1 (en) * | 2013-12-18 | 2015-06-18 | General Electric Company | System and method of x-ray dose distribution for computed tomography based on simulation |
US20160078619A1 (en) * | 2014-09-12 | 2016-03-17 | General Electric Company | Systems and methods for imaging phase selection for computed tomography imaging |
US20170128750A1 (en) * | 2014-07-25 | 2017-05-11 | Varian Medical Systems, Inc. | Imaging based calibration systems, devices, and methods |
US20170287173A1 (en) * | 2016-03-31 | 2017-10-05 | General Electric Company | Ct imaging apparatus and method, and x-ray transceiving component for ct imaging apparatus |
US20170291042A1 (en) * | 2016-04-12 | 2017-10-12 | Shimadzu Corporation | Positioning apparatus and method of positioning |
Families Citing this family (46)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8571289B2 (en) | 2002-11-27 | 2013-10-29 | Hologic, Inc. | System and method for generating a 2D image from a tomosynthesis data set |
US7787672B2 (en) | 2004-11-04 | 2010-08-31 | Dr Systems, Inc. | Systems and methods for matching, naming, and displaying medical images |
US7885440B2 (en) * | 2004-11-04 | 2011-02-08 | Dr Systems, Inc. | Systems and methods for interleaving series of medical images |
US7660488B2 (en) | 2004-11-04 | 2010-02-09 | Dr Systems, Inc. | Systems and methods for viewing medical images |
US7970625B2 (en) | 2004-11-04 | 2011-06-28 | Dr Systems, Inc. | Systems and methods for retrieval of medical data |
US7920152B2 (en) | 2004-11-04 | 2011-04-05 | Dr Systems, Inc. | Systems and methods for viewing medical 3D imaging volumes |
US10008184B2 (en) | 2005-11-10 | 2018-06-26 | Hologic, Inc. | System and method for generating a 2D image using mammography and/or tomosynthesis image data |
US8532745B2 (en) | 2006-02-15 | 2013-09-10 | Hologic, Inc. | Breast biopsy and needle localization using tomosynthesis systems |
US7953614B1 (en) | 2006-11-22 | 2011-05-31 | Dr Systems, Inc. | Smart placement rules |
US8147139B2 (en) | 2008-10-13 | 2012-04-03 | George Papaioannou | Dynamic biplane roentgen stereophotogrammetric analysis |
US8380533B2 (en) | 2008-11-19 | 2013-02-19 | DR Systems Inc. | System and method of providing dynamic and customizable medical examination forms |
US8737708B2 (en) | 2009-05-13 | 2014-05-27 | Medtronic Navigation, Inc. | System and method for automatic registration between an image and a subject |
US8238631B2 (en) * | 2009-05-13 | 2012-08-07 | Medtronic Navigation, Inc. | System and method for automatic registration between an image and a subject |
US8503745B2 (en) * | 2009-05-13 | 2013-08-06 | Medtronic Navigation, Inc. | System and method for automatic registration between an image and a subject |
US8712120B1 (en) | 2009-09-28 | 2014-04-29 | Dr Systems, Inc. | Rules-based approach to transferring and/or viewing medical images |
EP2485651B1 (en) | 2009-10-08 | 2020-12-23 | Hologic, Inc. | Needle breast biopsy system |
WO2011119960A1 (en) * | 2010-03-25 | 2011-09-29 | Beth Israel Deaconess Medical Center | System and method for frameless stereotactic radiosurgery of arteriovenous malformations |
EP2595542A1 (en) * | 2010-07-19 | 2013-05-29 | Koninklijke Philips Electronics N.V. | 3d-originated cardiac roadmapping |
US20120133600A1 (en) | 2010-11-26 | 2012-05-31 | Hologic, Inc. | User interface for medical image review workstation |
EP2465435B1 (en) * | 2010-12-14 | 2019-12-04 | General Electric Company | Selection of optimal viewing angle to optimize anatomy visibility and patient skin dose |
US9152766B2 (en) * | 2011-03-03 | 2015-10-06 | Brainlab Ag | Computer-assisted infusion planning and simulation |
EP2681712B1 (en) * | 2011-03-04 | 2019-06-19 | Koninklijke Philips N.V. | 2d/3d image registration |
EP2684157B1 (en) | 2011-03-08 | 2017-12-13 | Hologic Inc. | System and method for dual energy and/or contrast enhanced breast imaging for screening, diagnosis and biopsy |
DE102011076855B4 (en) * | 2011-06-01 | 2017-12-07 | Siemens Healthcare Gmbh | Method for the functional presentation and localization of an arteriovenous malformation, rotatable imaging system and combination of a rotatable imaging system and an irradiation unit |
US9075899B1 (en) | 2011-08-11 | 2015-07-07 | D.R. Systems, Inc. | Automated display settings for categories of items |
EP2782505B1 (en) | 2011-11-27 | 2020-04-22 | Hologic, Inc. | System and method for generating a 2d image using mammography and/or tomosynthesis image data |
WO2013123091A1 (en) | 2012-02-13 | 2013-08-22 | Hologic, Inc. | System and method for navigating a tomosynthesis stack using synthesized image data |
DE102012213456A1 (en) * | 2012-07-31 | 2014-02-06 | Siemens Aktiengesellschaft | Ultrasound sensor catheter and method of generating a volume graphic by means of the catheter |
US8983156B2 (en) * | 2012-11-23 | 2015-03-17 | Icad, Inc. | System and method for improving workflow efficiences in reading tomosynthesis medical image data |
US9495604B1 (en) | 2013-01-09 | 2016-11-15 | D.R. Systems, Inc. | Intelligent management of computerized advanced processing |
EP3366217B1 (en) | 2013-03-15 | 2019-12-25 | Hologic, Inc. | Tomosynthesis-guided biopsy in prone |
KR101572487B1 (en) * | 2013-08-13 | 2015-12-02 | 한국과학기술연구원 | System and Method For Non-Invasive Patient-Image Registration |
ES2878599T3 (en) | 2014-02-28 | 2021-11-19 | Hologic Inc | System and method to generate and visualize tomosynthesis image blocks |
GB201502877D0 (en) * | 2015-02-20 | 2015-04-08 | Cydar Ltd | Digital image remapping |
US20170039321A1 (en) | 2015-04-30 | 2017-02-09 | D.R. Systems, Inc. | Database systems and interactive user interfaces for dynamic interaction with, and sorting of, digital medical image data |
US10089756B2 (en) * | 2016-06-30 | 2018-10-02 | Zhiping Mu | Systems and methods for generating 2D projection from previously generated 3D dataset |
DE102016215971A1 (en) * | 2016-08-25 | 2018-03-01 | Siemens Healthcare Gmbh | Segmentation of angiography using an existing three-dimensional reconstruction |
JP6746435B2 (en) * | 2016-08-25 | 2020-08-26 | 株式会社東芝 | Medical image processing apparatus, treatment system, and medical image processing program |
CN110121290B (en) * | 2016-11-23 | 2023-02-17 | 通用电气公司 | Imaging protocol manager |
US10102640B2 (en) | 2016-11-29 | 2018-10-16 | Optinav Sp. Z O.O. | Registering three-dimensional image data of an imaged object with a set of two-dimensional projection images of the object |
JP7174710B2 (en) | 2017-03-30 | 2022-11-17 | ホロジック, インコーポレイテッド | Systems and Methods for Targeted Object Augmentation to Generate Synthetic Breast Tissue Images |
JP7169986B2 (en) | 2017-03-30 | 2022-11-11 | ホロジック, インコーポレイテッド | Systems and methods for synthesizing low-dimensional image data from high-dimensional image data using object grid augmentation |
EP3641635A4 (en) | 2017-06-20 | 2021-04-07 | Hologic, Inc. | Dynamic self-learning medical image method and system |
WO2020207597A1 (en) * | 2019-04-12 | 2020-10-15 | Brainlab Ag | Frameless anatomy-based 2d/3d image registration |
US11354800B2 (en) * | 2019-12-27 | 2022-06-07 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for error checking in radioitherapy treatment replanning |
US20220347491A1 (en) * | 2021-05-03 | 2022-11-03 | Washington University | Systems and methods of adaptive radiotherapy with conventional linear particle accelerator (linac) radiotherapy devices |
Citations (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4617925A (en) * | 1984-10-01 | 1986-10-21 | Laitinen Lauri V | Adapter for definition of the position of brain structures |
US5389101A (en) * | 1992-04-21 | 1995-02-14 | University Of Utah | Apparatus and method for photogrammetric surgical localization |
US5588033A (en) * | 1995-06-06 | 1996-12-24 | St. Jude Children's Research Hospital | Method and apparatus for three dimensional image reconstruction from multiple stereotactic or isocentric backprojections |
US6307914B1 (en) | 1998-03-12 | 2001-10-23 | Mitsubishi Denki Kabushiki Kaisha | Moving body pursuit irradiating device and positioning method using this device |
US6317621B1 (en) * | 1999-04-30 | 2001-11-13 | Siemens Aktiengesellschaft | Method and device for catheter navigation in three-dimensional vascular tree exposures |
US20020045817A1 (en) * | 2000-10-17 | 2002-04-18 | Masahide Ichihashi | Radiographic image diagnosis apparatus |
US20020136356A1 (en) * | 2001-03-22 | 2002-09-26 | Siemens Elema Ab | X-ray imaging system |
US20050013681A1 (en) * | 2003-06-20 | 2005-01-20 | Carvalho John F. | Non-current conducting nut |
US20050049486A1 (en) * | 2003-08-28 | 2005-03-03 | Urquhart Steven J. | Method and apparatus for performing stereotactic surgery |
US20060257006A1 (en) * | 2003-08-21 | 2006-11-16 | Koninklijke Philips Electronics N.V. | Device and method for combined display of angiograms and current x-ray images |
US20070009080A1 (en) * | 2005-07-08 | 2007-01-11 | Mistretta Charles A | Backprojection reconstruction method for CT imaging |
US20070110289A1 (en) * | 2005-11-16 | 2007-05-17 | Dongshan Fu | Rigid body tracking for radiosurgery |
US20070127845A1 (en) * | 2005-11-16 | 2007-06-07 | Dongshan Fu | Multi-phase registration of 2-D X-ray images to 3-D volume studies |
US7474913B2 (en) * | 2004-06-25 | 2009-01-06 | Siemens Aktiengesellschaft | Method for medical imaging |
US7739090B2 (en) * | 1998-02-03 | 2010-06-15 | University Of Illinois, Board Of Trustees | Method and system for 3D blood vessel localization |
US7894647B2 (en) * | 2004-06-21 | 2011-02-22 | Siemens Medical Solutions Usa, Inc. | System and method for 3D contour tracking of anatomical structures |
US7903856B2 (en) * | 2006-09-26 | 2011-03-08 | Siemens Aktiengesellschaft | Method for post-processing a three-dimensional image data set of vessel structure |
US8055044B2 (en) * | 2004-08-17 | 2011-11-08 | Koninklijke Philips Electronics N V | Flexible 3D rotational angiography and computed tomography fusion |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6259943B1 (en) * | 1995-02-16 | 2001-07-10 | Sherwood Services Ag | Frameless to frame-based registration system |
US7356113B2 (en) * | 2003-02-12 | 2008-04-08 | Brandeis University | Tomosynthesis imaging system and method |
US7570710B1 (en) | 2004-12-15 | 2009-08-04 | Rf Magic, Inc. | In-phase and quadrature-phase signal amplitude and phase calibration |
-
2007
- 2007-06-30 US US11/823,932 patent/US9427201B2/en active Active
-
2016
- 2016-07-26 US US15/219,514 patent/US11382588B2/en active Active
Patent Citations (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4617925A (en) * | 1984-10-01 | 1986-10-21 | Laitinen Lauri V | Adapter for definition of the position of brain structures |
US5389101A (en) * | 1992-04-21 | 1995-02-14 | University Of Utah | Apparatus and method for photogrammetric surgical localization |
US5588033A (en) * | 1995-06-06 | 1996-12-24 | St. Jude Children's Research Hospital | Method and apparatus for three dimensional image reconstruction from multiple stereotactic or isocentric backprojections |
US7739090B2 (en) * | 1998-02-03 | 2010-06-15 | University Of Illinois, Board Of Trustees | Method and system for 3D blood vessel localization |
US6307914B1 (en) | 1998-03-12 | 2001-10-23 | Mitsubishi Denki Kabushiki Kaisha | Moving body pursuit irradiating device and positioning method using this device |
US6317621B1 (en) * | 1999-04-30 | 2001-11-13 | Siemens Aktiengesellschaft | Method and device for catheter navigation in three-dimensional vascular tree exposures |
US20020045817A1 (en) * | 2000-10-17 | 2002-04-18 | Masahide Ichihashi | Radiographic image diagnosis apparatus |
US20020136356A1 (en) * | 2001-03-22 | 2002-09-26 | Siemens Elema Ab | X-ray imaging system |
US20050013681A1 (en) * | 2003-06-20 | 2005-01-20 | Carvalho John F. | Non-current conducting nut |
US20060257006A1 (en) * | 2003-08-21 | 2006-11-16 | Koninklijke Philips Electronics N.V. | Device and method for combined display of angiograms and current x-ray images |
US20050049486A1 (en) * | 2003-08-28 | 2005-03-03 | Urquhart Steven J. | Method and apparatus for performing stereotactic surgery |
US7894647B2 (en) * | 2004-06-21 | 2011-02-22 | Siemens Medical Solutions Usa, Inc. | System and method for 3D contour tracking of anatomical structures |
US7474913B2 (en) * | 2004-06-25 | 2009-01-06 | Siemens Aktiengesellschaft | Method for medical imaging |
US8055044B2 (en) * | 2004-08-17 | 2011-11-08 | Koninklijke Philips Electronics N V | Flexible 3D rotational angiography and computed tomography fusion |
US20070009080A1 (en) * | 2005-07-08 | 2007-01-11 | Mistretta Charles A | Backprojection reconstruction method for CT imaging |
US20070110289A1 (en) * | 2005-11-16 | 2007-05-17 | Dongshan Fu | Rigid body tracking for radiosurgery |
US20070127845A1 (en) * | 2005-11-16 | 2007-06-07 | Dongshan Fu | Multi-phase registration of 2-D X-ray images to 3-D volume studies |
US7684647B2 (en) * | 2005-11-16 | 2010-03-23 | Accuray Incorporated | Rigid body tracking for radiosurgery |
US7903856B2 (en) * | 2006-09-26 | 2011-03-08 | Siemens Aktiengesellschaft | Method for post-processing a three-dimensional image data set of vessel structure |
Non-Patent Citations (6)
Title |
---|
D. Gibon, Ph.D et al., "Stereotactic Localization in Medical Imaging: A Technical and Methodological Review", Journal of Radiosurgery, vol. 2, No. 3, 1999, Copyright 1999 Plenum Publishing Corporation, pp. 167-180. |
E Coste-Maniere et al., "Robotic Whole Body Stereotactic Radiosurgery: Clinical Advantages of the CyberKnife® Integrated System", Paper Accepted: Dec. 1, 2004, Published online: Jan. 15, 2005. Copyright 2005 Robotic Publications Ltd., Available from: www.roboticpublications.com, Int J Medical Robotics and Computer Assisted Surgery 2005; 1(2); 28-39. |
M. Vermandel et al., "A 2D/3D Matching Based on a Hybrid Approach: Improvement to the Imaging flow for AVM Radiosurgery", Proceedings of the 2005 IEEE, Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, Sep. 1-4, 2005, pp. 3071-3073. |
Maximilien Vermandel et al, "Registration, Matching, and Data Fusion in 2D/3D Medical Imaging: Application to DSA and MRA", Laboratoire de Biophysique-ITM, UPRES EA 1049, Pavillon Vancostenobel, University Hospital, F-59037 cedex, Lille, France, R.E. Ellis and T.M. Peters (Eds.): MICCAI 2003, LNCS 2878, pp. 778-785, 2003. Copyright Springer-Verlag Berlin Heidelberg 2003. |
Roger Y. Tsai, "A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses", IEEE Journal of Robotics and Automation, vol. RA-3, No. 4, Aug. 1987, Copyright 1987 IEEE, pp. 323-344. |
Zhengyou Zhang, Senior Member, IEEE, "A Flexible New Technique for Camera Calibration", IEEE Transactions on Pattern Analysis and Maching Intelligence, vol. 22, No. 11, Nov. 2000, pp. 1330-1334. |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150063537A1 (en) * | 2013-08-29 | 2015-03-05 | Samsung Electronics Co., Ltd. | X-ray imaging apparatus and control method thereof |
US9579071B2 (en) * | 2013-08-29 | 2017-02-28 | Samsung Electronics Co., Ltd. | X-ray imaging apparatus and control method thereof |
US9795356B2 (en) * | 2013-12-18 | 2017-10-24 | General Electric Company | System and method of X-ray dose distribution for computed tomography based on simulation |
US20150164457A1 (en) * | 2013-12-18 | 2015-06-18 | General Electric Company | System and method of x-ray dose distribution for computed tomography based on simulation |
US11324970B2 (en) * | 2014-07-25 | 2022-05-10 | Varian Medical Systems International Ag | Imaging based calibration systems, devices, and methods |
US20170128750A1 (en) * | 2014-07-25 | 2017-05-11 | Varian Medical Systems, Inc. | Imaging based calibration systems, devices, and methods |
US10507339B2 (en) * | 2014-07-25 | 2019-12-17 | Varian Medical Systems, Inc. | Imaging based calibration systems, devices, and methods |
US9517042B2 (en) * | 2014-09-12 | 2016-12-13 | General Electric Company | Systems and methods for imaging phase selection for computed tomography imaging |
US20160078619A1 (en) * | 2014-09-12 | 2016-03-17 | General Electric Company | Systems and methods for imaging phase selection for computed tomography imaging |
US20170287173A1 (en) * | 2016-03-31 | 2017-10-05 | General Electric Company | Ct imaging apparatus and method, and x-ray transceiving component for ct imaging apparatus |
US10517545B2 (en) * | 2016-03-31 | 2019-12-31 | General Electric Company | CT imaging apparatus and method, and X-ray transceiving component for CT imaging apparatus |
US20170291042A1 (en) * | 2016-04-12 | 2017-10-12 | Shimadzu Corporation | Positioning apparatus and method of positioning |
US10722733B2 (en) * | 2016-04-12 | 2020-07-28 | Shimadzu Corporation | Positioning apparatus and method of positioning |
Also Published As
Publication number | Publication date |
---|---|
US11382588B2 (en) | 2022-07-12 |
US20160331338A1 (en) | 2016-11-17 |
US20090005668A1 (en) | 2009-01-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11382588B2 (en) | Non-invasive method for using 2D angiographic images for radiosurgical target definition | |
US8086004B2 (en) | Use of a single X-ray image for quality assurance of tracking | |
US8090175B2 (en) | Target tracking using direct target registration | |
US8457372B2 (en) | Subtraction of a segmented anatomical feature from an acquired image | |
US8406851B2 (en) | Patient tracking using a virtual image | |
US7623623B2 (en) | Non-collocated imaging and treatment in image-guided radiation treatment systems | |
US20080037843A1 (en) | Image segmentation for DRR generation and image registration | |
US7620144B2 (en) | Parallel stereovision geometry in image-guided radiosurgery | |
US8417318B2 (en) | Calibrating tracking systems to remove position-dependent bias | |
US7831073B2 (en) | Precision registration of X-ray images to cone-beam CT scan for image-guided radiation treatment | |
US7907772B2 (en) | Delineation on three-dimensional medical image | |
US7302033B2 (en) | Imaging geometry for image-guided radiosurgery | |
US8315356B2 (en) | Image alignment | |
US20080021300A1 (en) | Four-dimensional target modeling and radiation treatment |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: ACCURAY INCORPORATED, CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WEST, JAY B.;MAURER, CALVIN R.;FU, DONGSHAN;AND OTHERS;SIGNING DATES FROM 20070820 TO 20070910;REEL/FRAME:019862/0765 Owner name: ACCURAY INCORPORATED, CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WEST, JAY B.;MAURER, CALVIN R.;FU, DONGSHAN;AND OTHERS;REEL/FRAME:019862/0765;SIGNING DATES FROM 20070820 TO 20070910 |
|
AS | Assignment |
Owner name: CERBERUS BUSINESS FINANCE, LLC, AS COLLATERAL AGENT, NEW YORK Free format text: ASSIGNMENT FOR SECURITY - PATENTS;ASSIGNORS:ACCURAY INCORPORATED;TOMOTHERAPY INCORPORATED;REEL/FRAME:037513/0170 Effective date: 20160111 Owner name: CERBERUS BUSINESS FINANCE, LLC, AS COLLATERAL AGEN Free format text: ASSIGNMENT FOR SECURITY - PATENTS;ASSIGNORS:ACCURAY INCORPORATED;TOMOTHERAPY INCORPORATED;REEL/FRAME:037513/0170 Effective date: 20160111 |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
AS | Assignment |
Owner name: MIDCAP FUNDING IV TRUST (AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FINANCIAL TRUST), MARYLAND Free format text: SECURITY INTEREST;ASSIGNORS:ACCURAY INCORPORATED;TOMOTHERAPY INCORPORATED;REEL/FRAME:042826/0358 Effective date: 20170614 Owner name: ACCURAY INCORPORATED, CALIFORNIA Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CERBERUS BUSINESS FINANCE, LLC. AS COLLATERAL AGENT;REEL/FRAME:042821/0580 Effective date: 20170614 Owner name: TOMOTHERAPY INCORPORATED, CALIFORNIA Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CERBERUS BUSINESS FINANCE, LLC. AS COLLATERAL AGENT;REEL/FRAME:042821/0580 Effective date: 20170614 Owner name: MIDCAP FUNDING IV TRUST (AS SUCCESSOR BY ASSIGNMEN Free format text: SECURITY INTEREST;ASSIGNORS:ACCURAY INCORPORATED;TOMOTHERAPY INCORPORATED;REEL/FRAME:042826/0358 Effective date: 20170614 |
|
AS | Assignment |
Owner name: MIDCAP FINANCIAL TRUST, MARYLAND Free format text: SECURITY INTEREST;ASSIGNORS:ACCURAY INCORPORATED;TOMOTHERAPY INCORPORATED;REEL/FRAME:044910/0685 Effective date: 20171215 |
|
AS | Assignment |
Owner name: MIDCAP FUNDING IV TRUST, AS SUCCESSOR TO EXISTING Free format text: ASSIGNMENT OF SECURITY AGREEMENTS;ASSIGNOR:MIDCAP FUNDING X TRUST (AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FUNDING IV TRUST, AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FINANCIAL TRUST), AS EXISTING ADMINISTRATIVE AGENT;REEL/FRAME:048481/0804 Effective date: 20190221 Owner name: MIDCAP FUNDING IV TRUST, AS SUCCESSOR TO EXISTING ADMINISTRATIVE AGENT, MARYLAND Free format text: ASSIGNMENT OF SECURITY AGREEMENTS;ASSIGNOR:MIDCAP FUNDING X TRUST (AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FUNDING IV TRUST, AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FINANCIAL TRUST), AS EXISTING ADMINISTRATIVE AGENT;REEL/FRAME:048481/0804 Effective date: 20190221 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1551); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 4 |
|
AS | Assignment |
Owner name: SILICON VALLEY BANK, AS ADMINISTRATIVE AND COLLATERAL AGENT, CALIFORNIA Free format text: SECURITY INTEREST;ASSIGNORS:ACCURAY INCORPORATED;TOMOTHERAPY INCORPORATED;REEL/FRAME:056247/0001 Effective date: 20210514 |
|
AS | Assignment |
Owner name: ACCURAY INCORPORATED, CALIFORNIA Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:MIDCAP FUNDING IV TRUST (AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FUNDING X TRUST, AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FUNDING IV TRUST, AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FINANCIAL TRUST);REEL/FRAME:056318/0559 Effective date: 20210514 Owner name: TOMOTHERAPY INCORPORATED, CALIFORNIA Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:MIDCAP FUNDING IV TRUST (AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FUNDING X TRUST, AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FUNDING IV TRUST, AS SUCCESSOR BY ASSIGNMENT FROM MIDCAP FINANCIAL TRUST);REEL/FRAME:056318/0559 Effective date: 20210514 Owner name: ACCURAY INCORPORATED, CALIFORNIA Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:MIDCAP FINANCIAL TRUST;REEL/FRAME:056318/0751 Effective date: 20210514 Owner name: TOMOTHERAPY INCORPORATED, CALIFORNIA Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:MIDCAP FINANCIAL TRUST;REEL/FRAME:056318/0751 Effective date: 20210514 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1552); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 8 |